def plot_one_boxplot_r(values, main="", logstring=""): from rpy import r if values.ndim == 1: v = resize(values, (1, values.size)) else: v = values r.boxplot(v[0,:], xlim=[0,v.shape[0]+1], ylim=r.c(values.min(), values.max()), range=0, log=logstring, main=main) for i in range(1, v.shape[0]): r.boxplot(v[i,:], at=i, add=True, range=0, log=logstring)
def create_p_value_boxplot_eps(best_p_values, filename): from rpy import r r.postscript(filename, horizontal=False, height=4.5, width=6, pointsize=10) try: keys = best_p_values.keys() keys.sort() r.boxplot( map(best_p_values.get, keys), names=map(str, keys), xlab="sample size", ylab="p-score") finally: r.dev_off()
def create_p_value_boxplot_eps(best_p_values, filename): from rpy import r r.postscript(filename, horizontal=False, height=4.5, width=6, pointsize=10) try: keys = best_p_values.keys() keys.sort() r.boxplot(map(best_p_values.get, keys), names=map(str, keys), xlab="sample size", ylab="p-score") finally: r.dev_off()
def plots(regression_o, getData_o): """Plots the dataset with a regression line and a boxplot using R.""" fname1 = 'car_regress.pdf' r.pdf(fname1) r.plot(getData_o, ylab='dist', xlab='speed') r.abline(regression_o['(Intercept)'], regression_o['y'], col='red') r.dev_off() fname2 = 'car_hist.pdf' r.pdf(fname2) r.boxplot(getData_o, names=['dist', 'speed']) r.dev_off() return fname1, fname2